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随机交叉−自学策略改进的教与学优化算法 被引量:5
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作者 黎延海 雍龙泉 拓守恒 《智能系统学报》 CSCD 北大核心 2021年第2期313-322,共10页
针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy,CSTLBO)。对标准教与学优化算... 针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy,CSTLBO)。对标准教与学优化算法的“教阶段”和“学阶段”的空间扰动进行了几何解释,改进了原有的“教阶段”和“学阶段”,并引入随机交叉策略和“自学”策略来提高算法的全局寻优能力。通过使用20个Benchmark函数进行仿真,并与6种改进的教与学优化算法进行结果比较及Wilcoxon秩和检验分析,结果表明CSTLBO算法能有效避免陷入局部最优,具有良好的全局搜索能力,求解精度高,稳定性好。 展开更多
关键词 群体智能 教与学优化 随机交叉 “自学”策略 Benchmark函数 非原点最优 多样性分析
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动态选择策略的和声教与学混合算法 被引量:5
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作者 黎延海 拓守恒 雍龙泉 《计算机应用研究》 CSCD 北大核心 2019年第12期3679-3684,共6页
为提高对多种不同类型问题的优化性能,提出了一种基于和声搜索和教与学优化的混合优化算法(HHSTL)。在不同的进化阶段,HHSTL算法依据种群活跃率及种群最优个体更新率动态地确定和声算法或教与学算法作为下一周期种群更新方式的比例,并... 为提高对多种不同类型问题的优化性能,提出了一种基于和声搜索和教与学优化的混合优化算法(HHSTL)。在不同的进化阶段,HHSTL算法依据种群活跃率及种群最优个体更新率动态地确定和声算法或教与学算法作为下一周期种群更新方式的比例,并在标准教与学算法中增加了"自学"策略来提高算法的全局寻优能力。对16个不同类型的Benchmark函数进行仿真,并与七种优秀算法进行结果比较及Wilcoxon秩和检验分析,结果表明HHSTL算法汲取了和声搜索和教与学优化算法的优点,具有求解精度高、稳定性好等特点,能够求解更多的较为复杂的优化问题。 展开更多
关键词 和声搜索 教与学优化 动态选择策略 “自学”策略
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Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state
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作者 CHENG Fang LIU Hui YANG Rui 《Journal of Central South University》 CSCD 2024年第11期4262-4275,共14页
Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a si... Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for TDGS.To overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is established.Among them,hyperparameter optimization algorithm’s effectiveness is confirmed through test functions.Subsequently,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is developed.Finally,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing models.Additionally,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based PDEM.The reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,respectively.The TDGS mean management index,i.e.,track quality index,has reliability value of 0.9950.These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance. 展开更多
关键词 reliability assessment track dynamic geometric state hybrid machine learning algorithm adaptive learning strategy probability density evolution method
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